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1. Identity statement
Reference TypeBook Section
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W/48742P5
Repositorysid.inpe.br/plutao/2022/12.12.17.39.21   (restricted access)
Last Update2022:12.15.14.28.58 (UTC) lattes
Metadata Repositorysid.inpe.br/plutao/2022/12.12.17.39.22
Metadata Last Update2023:01.03.16.52.57 (UTC) administrator
DOI10.3390/books978-3-0365-5668-0
ISBN9783036556
Labellattes: 8734553235868564 2 ShimabukuroDuArDuCaPeCa:2022:MaBuAr
Citation KeyShimabukuroDuArDuCaPeCa:2022:MaBuAr
TitleMapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets
Year2022
Access Date2024, May 18
Secondary TypePRE LI
Number of Files1
Size11105 KiB
2. Context
Author1 Shimabukuro, Yosio Edemir
2 Dutra, Andeise Cerqueira
3 Arai, Egidio
4 Duarte, Valdete
5 Cassol, Henrique Luís Godinho
6 Pereira, Gabriel
7 Cardozo, Francielle da Silva
Resume Identifier1 8JMKD3MGP5W/3C9JJCQ
2
3 8JMKD3MGP5W/3C9JGUP
4 8JMKD3MGP5W/3C9JJAU
Group1 DIOTG-CGCT-INPE-MCTI-GOV-BR
2 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
3 DIOTG-CGCT-INPE-MCTI-GOV-BR
4 DIOTG-CGCT-INPE-MCTI-GOV-BR
5 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
6 Universidade Federal de São João Del Rei
7 Universidade Federal de São João Del Rei
Author e-Mail Address1 yosio.shimabukuro@inpe.br
2 andeise.dutra@inpe.br
3 egidio.arai@inpe.br
4 valdete.duarte@inpe.br
5 henrique.cassol@inpe.br
6 pereira@ufsj.edu.br
7 franciellecardozo@ufsj.edu.br
EditorFernández-Manso, A.
Quintano, C.
Book TitleAdvances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics
PublisherMDPI
CityBasel
Pages115-137
History (UTC)2022-12-12 17:39:22 :: lattes -> administrator ::
2022-12-13 10:47:49 :: administrator -> lattes :: 2022
2022-12-15 14:29:00 :: lattes -> administrator :: 2022
2022-12-20 10:35:21 :: administrator -> lattes :: 2022
2022-12-20 13:36:48 :: lattes -> administrator :: 2022
2023-01-03 16:52:57 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsburned areas detection
shade fraction image
linear spectral mixing model
VIIRS

PROBA-V
Landsat-8 OL
AbstractQuantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board AutonomyVegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Mapping Burned Areas...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Mapping Burned Areas...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languageen
Target Fileshimabukuro_mapping.pdf
User Grouplattes
Visibilityshown
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
URL (untrusted data)https://www.mdpi.com/books/book/6270-advances-in-remote-sensing-of-postfire-environmental-damage-and-recovery-dynamics
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage e-mailaddress edition format issn lineage mark mirrorrepository nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarykey secondarymark serieseditor seriestitle session shorttitle sponsor subject tertiarymark tertiarytype translator volume
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